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Publication# Stochastic approximation methods for PDE constrained optimal control problems with uncertain parameters

Abstract

This thesis work focuses on optimal control of partial differential equations (PDEs) with uncertain parameters, treated as a random variables. In particular, we assume that the random parameters are not observable and look for a deterministic control which is robust with respect to the randomness. The theoretical framework is based on adjoint calculus to compute the gradient of the objective functional. Unlike the deterministic case, we have a set of PDEs indexed by some uncertain parameters and the objective functional we consider includes some risk measure (e.g. expectation, variance, quantile (Value at Risk), ...) to take care of all realizations of our uncertain parameters. Introducing the regular class of coherent risk measure, results of convergence and regularity of the optimal control have been derived.

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In financial mathematics, a risk measure is used to determine the amount of an asset or set of assets (traditionally currency) to be kept in reserve. The purpose of this reserve is to make the risks taken by financial institutions, such as banks and insurance companies, acceptable to the regulator. In recent years attention has turned towards convex and coherent risk measurement. A risk measure is defined as a mapping from a set of random variables to the real numbers. This set of random variables represents portfolio returns.

In the fields of actuarial science and financial economics there are a number of ways that risk can be defined; to clarify the concept theoreticians have described a number of properties that a risk measure might or might not have. A coherent risk measure is a function that satisfies properties of monotonicity, sub-additivity, homogeneity, and translational invariance. Consider a random outcome viewed as an element of a linear space of measurable functions, defined on an appropriate probability space.

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